What is he best known for?
The fields of study he is best known for:
- Control theory
- Quantum mechanics
- Statistics
James B. Rawlings mainly investigates Control theory, Model predictive control, Mathematical optimization, Optimal control and Nonlinear system.
The Control theory study combines topics in areas such as Estimator and Process.
James B. Rawlings has researched Model predictive control in several fields, including Stability, Control system, Process control and Control engineering.
His research integrates issues of Kalman filter, Exponential stability, Linear model and Linear system in his study of Mathematical optimization.
His biological study spans a wide range of topics, including Optimization problem, MATLAB, Modeling language and Sensitivity.
As part of one scientific family, James B. Rawlings deals mainly with the area of Nonlinear system, narrowing it down to issues related to the Moving horizon estimation, and often Robustness, Nonlinear predictive control and Time domain.
His most cited work include:
- Survey Constrained model predictive control: Stability and optimality (6379 citations)
- The stability of constrained receding horizon control (904 citations)
- Tutorial overview of model predictive control (846 citations)
What are the main themes of his work throughout his whole career to date?
James B. Rawlings focuses on Control theory, Model predictive control, Mathematical optimization, Nonlinear system and Optimal control.
His work investigates the relationship between Control theory and topics such as Estimator that intersect with problems in Extended Kalman filter.
His studies in Model predictive control integrate themes in fields like Control system, Stability, Control engineering, Control theory and Process control.
His research in the fields of Quadratic programming, Optimization problem and Linear programming overlaps with other disciplines such as State.
His work carried out in the field of Nonlinear system brings together such families of science as Kalman filter and Horizon.
The concepts of his Optimal control study are interwoven with issues in Automatic control and System identification.
He most often published in these fields:
- Control theory (50.21%)
- Model predictive control (44.64%)
- Mathematical optimization (38.63%)
What were the highlights of his more recent work (between 2013-2021)?
- Mathematical optimization (38.63%)
- Model predictive control (44.64%)
- Control theory (50.21%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Mathematical optimization, Model predictive control, Control theory, HVAC and Control engineering.
His Mathematical optimization research includes elements of Economic model predictive control and Constraint.
James B. Rawlings performs multidisciplinary study in the fields of Model predictive control and Upper and lower bounds via his papers.
When carried out as part of a general Control theory research project, his work on Robustness, Optimal control, Nonlinear system and Exponential stability is frequently linked to work in Bounded function, therefore connecting diverse disciplines of study.
His Nonlinear system study integrates concerns from other disciplines, such as Stochastic process and Reduction.
In his research, Hierarchical control system, Real-time Control System, System identification, Moving horizon estimation and Observability is intimately related to Scale, which falls under the overarching field of Control engineering.
Between 2013 and 2021, his most popular works were:
- CasADi: a software framework for nonlinear optimization and optimal control (543 citations)
- Economic model predictive control for inventory management in supply chains (52 citations)
- Economic MPC and real-time decision making with application to large-scale HVAC energy systems (40 citations)
In his most recent research, the most cited papers focused on:
- Quantum mechanics
- Statistics
- Control theory
His primary areas of study are Mathematical optimization, Control theory, Model predictive control, Nonlinear system and Optimization problem.
His work in the fields of Mathematical optimization, such as Scheduling, overlaps with other areas such as Upper and lower bounds.
His work in the fields of Stability, Exponential stability and Optimal control overlaps with other areas such as State and Bounded function.
His research in Model predictive control intersects with topics in Piecewise linear function, Discrete time and continuous time, Quadratic programming, Linear-quadratic-Gaussian control and Linear-quadratic regulator.
His Nonlinear system study incorporates themes from Statistical physics, Reduction and Robustness.
His Optimization problem research is multidisciplinary, incorporating elements of Chiller, Modeling language, Linear programming, MATLAB and HVAC.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
